Artificial neural networks (NNs) are revolutionizing the field of computer vision. The top-ranking algorithms in various visual object recognition challenges, including ImageNet, Microsoft COCO, and Pascal VOC, are all based on NNs.
In the visual object recognition using the NNs, the large scale image datasets are used for training the NNs to obtain better performance. However, annotating large-scale image datasets is an expensive and tedious task, requiring people to spend a large amount of time analyzing image content in a dataset because the subset of important images in the unlabeled dataset are selected and labelled by the human annotations.
Active learning is a machine learning procedure that is useful in reducing the amount of annotated data required to achieve a target performance. It has been applied to various computer-vision problems including object classification and activity recognition. The active learning starts by training a baseline model (object detector, such as neural network) with a small, labelled dataset, and then applying the object detector to the unlabeled data. For unlabeled samples, the active learning estimates whether each unlabeled sample contains critical information that has not been learned by the baseline model. Once the unlabeled samples that bring the most critical information are identified and labelled by human annotators, the labelled samples can be added to the initial training dataset to retrain the model. Compared to passive learning, which randomly selects samples from the unlabeled dataset, the active learning can achieve the same accuracies with fewer but more informative labelled samples. See, e.g., U.S. Ser. No. 15/691,911.
Different active learning methods use different metrics for measuring how informative a sample is for the classification task. Examples of such metrics include the maximum uncertainty, expected model change, density weighted metric, etc. However, those metrics are still suboptimal for some applications. Accordingly, there is a need to develop optimal metrics of the active learning methods for other applications.